Currency has great importance in day-to-day life and therefore currency recognition is a great area of interest for researchers. Image processing is the most popular and effective method of currency recognition. Image processing-based currency recognition technique consists of a few basic steps like image acquisition, its preprocessing and finally recognition of the currency. To recognize a character from a given currency image, there is a need to extract feature descriptors of such image. As extraction method significantly affects the quality of whole Optical Character Recognition (OCR) process, it is very important to extract features, which will be invariant towards various light conditions, used font type and deformations of characters caused by a skew of the image. Heuristic analysis of characters is done for this purpose to get the accurate features of characters before feature extraction in currency.

Description

Feature extraction of images is a challenging work in digital image processing. Feature extraction of Indian currency notes involves the extraction of features of serial numbers of currency notes (Khotanzad and Hong, 1990a; and Foresti and Regazzoni, 2009). This extracts information from the raw data which is most relevant for the identification purpose, during which the dimensionality of the data gets reduced. This is almost and always necessary due to technical limit in memory and computational time (Parminder et al., 2011). A good feature extraction scheme should maintain and enhance those features of the input data which make distinct pattern classes separate from each other. At the same time, the system should be immune to variations produced due to humans using it and the technical devices used in the data acquisition (Ji Qian et al., 2006).

In recent years, along with the accelerative developments of the world economics incorporation course, the start of euro area, and the rise of Asian economies, frontier trade and personal intercourse of various countries are frequently increasing. Travelling people always take paper currencies of many countries (Khotanzad and Hong, 1990b). Probabilities that the paper currencies of various countries are properly interleaved together therefore rise increasingly. It is a challenge for conventional paper currency system. However, the focus of most of the conventional currency recognition system and machines is, recognizing counterfeit currency (Ramanjit and Priyadarshni, 2016). It is not enough for practical businesses. The reason is that in most banks, especially, in the international banks, there are large quantities of cash belonging to many different countries needed to be processed and it is possible that all of them are real cash (Ji Qian et al., 2006; and Parminder et al., 2011).